Borrowing it
Nothing to install: this file belongs to rollinsio/beyond-test-coverage. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rollinsio/beyond-test-coverage/main/.claude/skills/results-dashboard/SKILL.mdgit clone --depth 1 https://github.com/rollinsio/beyond-test-coverageWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rollinsio/beyond-test-coverage/results-dashboard)<a href="https://agentmods.dev/skills/rollinsio/beyond-test-coverage/results-dashboard"><img src="https://agentmods.dev/badge/skills/rollinsio/beyond-test-coverage/results-dashboard.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.00786 |
| Opus 5 | $0.00060 | $0.00393 |
| Sonnet 5 | $0.00024 | $0.00157 |
| Haiku 4.5 | $0.00012 | $0.00079 |
Grade A, and why
results-dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
results-dashboard
Render a scorecard JSON into one self-contained HTML page you can open in a
browser or commit under docs/. The input is the {baselines, arms} shape the
benchmark's scorers emit (see references/scorecard-json-shape.md); the output
is a dark-themed readout with:
- Hero stats — arms scored, arms that beat their baseline (wins > losses),
and a per-language split when arms carry a
lang. - Per-axis Win / Tie / Loss chart — a stacked bar per quality axis across all arms, so you can see at a glance which axes the generated suites win or lose.
- Per-suite matrix — every arm × every axis; each cell is
genoverbasewith ✓ win / ✗ loss / = tie / · n-a, plus the W/L/T tally and a better? badge.
When to use
- "Regenerate the results dashboard / readout for
<scorecard>.json." - "Visualize these scorecard results as a web page."
- After running
score_quality.py/score_cross_language.py(or thetest-qualityskill'sscore.py --json …), to publish the numbers.
Procedure
- Locate the input. A scorecard JSON with a top-level
armsarray (and usuallybaselines). If the user points at aresults-*-scorecard.json, use it directly. If they only have a raw tests dir, run the relevant scorer first to produce the JSON, then feed it here. - Confirm the framing. Ask for (or infer) a
--titleand a one-line--subtitle. Default title is derived from the filename. - Generate:
python <skill>/scripts/build_dashboard.py <scorecard>.json \ -o docs/<name>.html --title "…" --subtitle "…" - Verify. Open the file (or screenshot it) and check the hero counts match the scorer's own summary line (arms present / beat baseline). The script prints those counts on success — they must agree with the JSON.
Notes
- Self-contained. The only external dependency is Chart.js from a CDN; the page needs network to draw the chart but renders the matrix offline.
- Shape tolerance. Arms with
"present": falserender as anabsentrow;nullaxis values render as·. Axis keys are read from the first present arm, so the columns adapt to whichever axes the scorer measured. - Don't hand-edit generated HTML. Re-run the script after the scorer changes;
the committed
docs/*.htmldashboards in this repo are curated, richer variants of the same idea — treat this skill's output as the reproducible base.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 65 lines · 119 tokens per session scan A 11c4197abe0b
results-dashboard is a skill published in the GitHub repository rollinsio/beyond-test-coverage (52 stars, last pushed 2mo ago), licensed MIT. It adds 119 tokens to every session and 786 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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